The purpose of this document is to highlight high-level facts about crashes that injured or killed pedestrians in DC during the last several years. The document will not focus on crashes that injured motorists or bicyclists. (Such crashes may be included in the analysis, however, if those crashes also happened to involve pedestrian injuries or deaths).
The data presented in this document should be interpreted with extreme caution, because,
The bar chart below shows the number of pedestrian injuries between 2018 and 2022 by ward. Injuries seem to be especially high in ward 2. The relative differences between the wards are largely unchanged if we divide the number of injuries by the ward population. It is possible that some of the injured pedestrians in ward 2 are tourists visiting from out of town. This analysis doesn’t account for the number of pedestrian-miles-walked in the ward during the time period, or differentiate between DC residents and visitors.
Ward 2 has the highest number of pedestrian injuries, but wards 5 and 8 have the highest numbers of pedestrian deaths. Again, these relative differences hold even after accounting for ward population. This suggests that crashes in wards 5 and 8 tend to be more deadly than in ward 2, for example.
The heatmap below shows where crashes that injure pedestrians tended to occur over the 16 years, based on the CrashesInDC data. The red dots show crashes that killed pedestrians. If you hover over the points, you can see the year in which that person died.
It looks like these crashes tend to occur near intersections (perhaps while people are trying to cross the street). By contrast, these crashes seem less likely to occur near the middle of blocks. Many crashes seem to occur downtown, perhaps because there are more people walking there.
The analysis below draws 20 meter buffers around each crash, then spatially joins each crash to another spatial data set of roads in DC. The road data set contains information about the speed limit on each street. With additional time, this analysis could be much improved, to account for the prevalence of each speed limit in the District, for example. This could help answer questions such as “are there disproportionately more crashes on 30-mph streets, given the number of pedestrian-car interactions on those streets?”
Note that I have omitted 20-mph streets from the chart below, because DDOT appears to code the speed limit “20-mph” onto street segments with missing information.